| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment |
| Authors: | Uzdilli, Fatih Jaggi, Martin Egger, Dominic Julmy, Pascal Derczynski, Leon Cieliebak, Mark |
| DOI: | 10.18653/v1/S15-2101 |
| Proceedings: | Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015 |
| Volume(Issue): | 9 |
| Page(s): | 608 |
| Pages to: | 612 |
| Conference details: | International Workshop on Semantic Evaluation (SemEval-2014), Dublin, Irland, 23-24 August 2014 |
| Issue Date: | 2015 |
| Publisher / Ed. Institution: | Association for Computational Linguistics |
| Language: | English |
| Subject (DDC): | 004: Computer science 005: Computer programming, programs and data |
| Abstract: | We describe a classifier for predicting message-level sentiment of English microblog messages from Twitter. This paper describes our submission to the SemEval-2015 competition (Task 10). Our approach is to combine several variants of our previous year’s SVM system into one meta-classifier, which was then trained using a random forest. The main idea is that the meta-classifier allows the combination of the strengths and overcome some of the weaknesses of the artificially-built individual classifiers, and adds additional non-linearity. We were also able to improve the linear classifiers by using a new regularization technique we call flipout. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/1881 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
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Uzdilli, F., Jaggi, M., Egger, D., Julmy, P., Derczynski, L., & Cieliebak, M. (2015). Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment [Conference paper]. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, 9, 608–612. https://doi.org/10.18653/v1/S15-2101
Uzdilli, F. et al. (2015) ‘Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment’, in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015. Association for Computational Linguistics, pp. 608–612. Available at: https://doi.org/10.18653/v1/S15-2101.
F. Uzdilli, M. Jaggi, D. Egger, P. Julmy, L. Derczynski, and M. Cieliebak, “Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment,” in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, 2015, vol. 9, pp. 608–612. doi: 10.18653/v1/S15-2101.
UZDILLI, Fatih, Martin JAGGI, Dominic EGGER, Pascal JULMY, Leon DERCZYNSKI und Mark CIELIEBAK, 2015. Swiss-Chocolate : combining flipout regularization and random forests with artificially built subsystems to boost text-classification for sentiment. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015. Conference paper. Association for Computational Linguistics. 2015. S. 608–612
Uzdilli, Fatih, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, and Mark Cieliebak. 2015. “Swiss-Chocolate : Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment.” Conference paper. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, 9:608–12. Association for Computational Linguistics. https://doi.org/10.18653/v1/S15-2101.
Uzdilli, Fatih, et al. “Swiss-Chocolate : Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment.” Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, vol. 9, Association for Computational Linguistics, 2015, pp. 608–12, https://doi.org/10.18653/v1/S15-2101.
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